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Neural_Network.py
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Neural_Network.py
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import numpy as np
from typing import Dict, List, Tuple
from sklearn.preprocessing import StandardScaler
class NeuralNetwork:
def __init__(self, X: np.array,
Y: np.array,
hidden_layer: int = 4,
epochs: int = 1000,
learning_rate: int = 0.01,
standardize: bool = False) -> None:
print("\n Welcome To Lk Nueral Network \n ")
self.X: np.array = X
self.Y: np.array = Y
self.hidden_layers: int = hidden_layer
self.epochs: int = epochs
self.learning_rate: int = learning_rate
self.standardize: bool = standardize
self.layers: Tuple = self._layer_size(self.X, self.Y)
self.parameters: Dict[np.array] = None
self.cache: Dict[np.array] = None
self.grads: Dict[np.array] = None
self.cost: int = None
def _layer_size(self, x: np.array, y: np.array) -> Tuple[int]:
n_x = self.X.shape[0]
n_h = self.hidden_layers
n_y = self.Y.shape[0]
'''return n_x, n_h, n_y order'''
return n_x, n_h, n_y
def sigmoid(self, z) -> np.array:
return 1/(1+np.exp(-z))
def initialize_parameters(self):
np.random.seed(2)
W1:np.array = np.random.randn(self.layers[1], self.layers[0]) * 0.01
b1:np.array = np.zeros(shape=(self.layers[1], 1))
W2:np.array = np.random.randn(self.layers[2], self.layers[1]) * 0.01
b2:np.array = np.zeros(shape=(self.layers[2], 1))
parameter: Dict[np.array] = {'W1': W1,
'b1': b1,
'W2': W2,
'b2': b2}
self.parameters = parameter
def foward_propagate(self):
W1 = self.parameters['W1']
b1 = self.parameters['b1']
W2 = self.parameters['W2']
b2 = self.parameters['b2']
Z1 = (np.dot(W1, self.X)) + b1
A1 = np.tanh(Z1)
Z2 = (np.dot(W2, A1)) + b2
A2 = self.sigmoid(Z2)
assert(A2.shape == (1, self.X.shape[1])), "check there is somthing wrong"
cache: Dict[np.array] = {'Z1': Z1,
'Z2': Z2,
'A1': A1,
'A2': A2}
self.cache = cache
return A2
def backward_propagate(self):
m = self.X.shape[1]
W1 = self.parameters['W1']
W2 = self.parameters['W2']
A1 = self.cache['A1']
A2 = self.cache['A2']
dz2 = A2 - self.Y
dW2 = 1/m * np.dot(dz2, A1.T)
db2 = 1/m * np.sum(dz2, axis=1, keepdims=True)
dz1 = np.multiply(np.dot(W2.T, dz2), (1- np.power(A1, 2)))
dW1 = 1/m * np.dot(dz1, self.X.T)
db1 = 1/m * np.sum(dz1, axis=1, keepdims=True)
grads = {'dW1': dW1,
'dW2': dW2,
'db1': db1,
'db2': db2}
self.grads = grads
def compute_cost(self):
m = self.Y.shape[1]
A2 = self.cache['A2']
cost = (-1/m) * np.sum(self.Y*np.log(A2) + (1-self.Y) * np.log(1-A2))
self.cost = cost
def update_parameter(self):
W1 = self.parameters['W1'] - self.learning_rate * self.grads['dW1']
b1 = self.parameters['b1'] - self.learning_rate * self.grads['db1']
W2 = self.parameters['W2'] - self.learning_rate * self.grads['dW2']
b2 = self.parameters['b2'] - self.learning_rate * self.grads['db2']
parameter: Dict[np.array] = {'W1': W1,
'b1': b1,
'W2': W2,
'b2': b2}
self.parameters = parameter
def fit(self):
np.random.seed(2)
self.initialize_parameters()
if self.standardize:
self.X = StandardScaler().fit_transform(self.X)
for i in range(0, self.epochs):
self.foward_propagate()
self.compute_cost()
self.backward_propagate()
self.update_parameter()
if i % 1000 == 0:
print(f'Epochs = {i}, Cost = {self.cost}')
print(self.parameters)
return self.parameters
def predict(self, data, parameters) -> float:
def foward_propagate(X, parameters):
W1 = parameters['W1']
b1 = parameters['b1']
W2 = parameters['W2']
b2 = parameters['b2']
Z1 = (np.dot(W1, X)) + b1
A1 = np.tanh(Z1)
Z2 = (np.dot(W2, A1)) + b2
A2 = self.sigmoid(Z2)
return A2
A2 = foward_propagate(data, parameters)
return A2